Distributed Detection of Sensor Worms Using Sequential Analysis and Remote Software Attestations

Recent work has demonstrated that self-propagating worms are a real threat to sensor networks. Since worms can enable an adversary to quickly compromise an entire sensor network, they must be detected and stopped as quickly as possible. To meet this need, we propose a worm propagation detection scheme for sensor networks. The proposed scheme applies a sequential analysis to detect worm propagation by leveraging the intuition that a worm’s communication pattern is different from benign traffic. In particular, a worm in a sensor network requires a long sequence of packets propagating hop-by-hop to each new infected node in turn. We thus have detectors that observe communication patterns in the network, a worm spreading hop-by-hop will quickly create chains of connections that would not be seen in normal traffic. Once detector nodes identify the worm propagation pattern, they initiate remote software attestations to detect infected nodes. Through analysis and simulation, we demonstrate that the proposed scheme effectively and efficiently detects worm propagation. In particular, it blocks worm propagation while restricting the fraction of infected nodes to at most 13.5% with an overhead of at most 0.63 remote attestations per node per time slot

Execution transactions for defending against software failures: use and evaluation

We examine the problem of containing buffer overflow attacks in a safe and efficient manner. Briefly, we automatically augment source code to dynamically catch stack and heap-based buffer overflow and underflow attacks, and recover from them by allowing the program to continue execution. Our hypothesis is that we can treat each code function as a transaction that can be aborted when an attack is detected, without affecting the application's ability to correctly execute. Our approach allows us to enable selectively or disable components of this defensive mechanism in response to external events, allowing for a direct tradeoff between security and performance. We combine our defensive mechanism with a honeypot-like configuration to detect previously unknown attacks, automatically adapt an application's defensive posture at a negligible performance cost, and help determine worm signatures. Our scheme provides low impact on application performance, the ability to respond to attacks without human intervention, the capacity to handle previously unknown vulnerabilities, and the preservation of service availability. We implement a stand-alone tool, DYBOC, which we use to instrument a number of vulnerable applications. Our performance benchmarks indicate a slow-down of 20% for Apache in full-protection mode, and 1.2% with selective protection. We provide preliminary evidence toward the validity of our transactional hypothesis via two experiments: first, by applying our scheme to 17 vulnerable applications, successfully fixing 14 of them; second, by examining the behavior of Apache when each of 154 potentially vulnerable routines are made to fail, resulting in correct behavior in 139 cases (90%), with similar results for sshd (89%) and Bind (88%)

Cybersecurity for Manufacturers: Securing the Digitized and Connected Factory

As manufacturing becomes increasingly digitized and data-driven, manufacturers will find themselves at serious risk. Although there has yet to be a major successful cyberattack on a U.S. manufacturing operation, threats continue to rise. The complexities of multi-organizational dependencies and data-management in modern supply chains mean that vulnerabilities are multiplying. There is widespread agreement among manufacturers, government agencies, cybersecurity firms, and leading academic computer science departments that U.S. industrial firms are doing too little to address these looming challenges. Unfortunately, manufacturers in general do not see themselves to be at particular risk. This lack of recognition of the threat may represent the greatest risk of cybersecurity failure for manufacturers. Public and private stakeholders must act before a significant attack on U.S. manufacturers provides a wake-up call. Cybersecurity for the manufacturing supply chain is a particularly serious need. Manufacturing supply chains are connected, integrated, and interdependent; security of the entire supply chain depends on security at the local factory level. Increasing digitization in manufacturing— especially with the rise of Digital Manufacturing, Smart Manufacturing, the Smart Factory, and Industry 4.0, combined with broader market trends such as the Internet of Things (IoT)— exponentially increases connectedness. At the same time, the diversity of manufacturers—from large, sophisticated corporations to small job shops—creates weakest-link vulnerabilities that can be addressed most effectively by public-private partnerships. Experts consulted in the development of this report called for more holistic thinking in industrial cybersecurity: improvements to technologies, management practices, workforce training, and learning processes that span units and supply chains. Solving the emerging security challenges will require commitment to continuous improvement, as well as investments in research and development (R&D) and threat-awareness initiatives. This holistic thinking should be applied across interoperating units and supply chains.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/145442/1/MForesight_CybersecurityReport_Web.pd

Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach

Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.publishedVersio